The leader in the BI analytics platform sphere, and the preferred BI tool by Vortisieze, MicroStrategy is now allowing users to enjoy the richness of data that Hadoop allows.
- MicroStrategy Inc.’s has launched a major upgrade to its namesake business intelligence platform that promises to help organizations make more out of the vast troves of data they’re storing in Hadoop. That’s done through improved connector bundled natively into the new release.
- MicroStrategy 10 enables analysts to manipulate imported information using new search and visualization functionality that the company says removes the need to use external tools for that job. That avoids the overhead associated with moving files back and forth, which can amount to significant time savings at the petabyte scale in which many production-grade Hadoop clusters operate.
- The company promises to take even more delay out of the equation with optimizations to its multi-dimensional analytic algorithms, which now makes it possible to put more data into the logical constructs used to group similar metrics for faster queries. MicroStrategy said that the update has helped PayPal, Inc. achieve sub-second response times for 400-gigabyte workloads.
- Analysts can take advantage of that performance through the improved interface also introduced in the new release, which is extended to mobile devices with a new companion app that can track the the performance of each user. The client also includes a badge-based security system that allows administrators to control who can access what and how.
Source: MicroStrategy 10 brings new analytic capabilities to the Hadoop table
As Business Intelligence grows in importance within many large and medium-sized organizations there are many issues surrounding the data that an organization has to deal with in order to improve its decision making processes. One of the most important is data quality which is frequently highlighted by Business Intelligence.
Comprehensive management of data quality is a crucial part of any Business Intelligence endeavor. It is important to address all types of data quality issues and come up with an all-in-one solution.
A Single (Trusted) Version of the Truth
- Governing data quality ensures trust in your information, fixing data problems during the extraction, transformation and loading process, and creating policies to know when data is an outlier.
- VortiSieze software supports the consistent accuracy of complete data so you can focus on making more informed decisions and gain efficiencies in your business processes.
- Supporting growth, innovation and compliance is based your ability to make crucial business decisions which suffers when you lack credible information.
- Ensuring a successful data management initiative requires carefully planning for data quality, i.e. accuracy.
- A carefully planned data quality initiative is essential to any successful data management initiative – be it a business intelligence (BI) or data warehousing (DW) project, a new implementation of a customer relationship management (CRM) system, or a data migration (DM) project.
- You can be more confident in your business decisions by taking the necessary steps to provide complete and reliable data.
Data Cleansing Delivers Data You Can Trust
- With VortiSieze, parsing, standardizing and cleansing data, from any domain, source or type, is functionality built into the solution.
- Parsing data identifies individual elements and breaks those into components. These are rearranged into a single field or move may elements from a single field into many, unique fields.
- Once parsed, your data is check for consistency, preparing for validation, correction, and accurate record matching.
- Your data is standardized using business rules that defines formatting, abbreviations, acronyms, punctuation, greetings, casing, order, and pattern matching – placing you in control according to your business needs.
- Dirty data (data with incorrect elements) is cleansed by correcting or adding missing elements and is done on a wide variety of data types
Enhancing Data Gives Your Greater Insight and Opportunity
- You can maximize the value of your data by enhancing data with internal or external sources, i.e. enriching your existing data set by appending additional data to it.
- This provides a more complete view of your data that can help you, for example, more effectively target customers and prospects, take advantage of cross-selling opportunities, and gain deeper insights into your business.
- With VortiSieze, enhancement options include:
- Weather data to predict long term trends in agriculture.
- Commodity prices to aid in negotiating with a valued distributor or retailer.
- Planogram or modular data to enhance shelf display planning.
- Geocoding longitude and latitude information to records for marketing initiatives that are geographically or demographically based.
- Geospatial assignment of customer addresses for tax jurisdictions, insurance rating territories, and insurance hazards.
Uncover Real Issues with Data Input, Matching and Consolidation
- Consolidate data to uncover hidden relationships and provide a single version of the truth.
- Incorrect data creates problems that flow ‘downstream’ making it difficult to identify the correct entity to enter new information against and to verify even basic information such as how many customers you have, which products they own, and which products come from which suppliers.
- Duplicate data presents a myriad of issues and it becomes difficult to:
- Identify the correct data to key new information against
- Verify even basic quantitative information on customers, products, or which products come from which suppliers.
- Duplicate records can exist in more than one source systems; data matching algorithms within VortiSieze can reduce or eliminate duplicate data.
Governing Data With Data Quality Measures
- VortiSieze software helps you to analyze and understand how trustworthy is your enterprise information.
- You will also get continuous insight into the quality of your data.
You Make Better Decisions with Reliable Data That is Trusted
- VortiSieze empowers you to enhance data quality for effective decision making and business operations.
- You can easily find data outliers and as these arise correct the issue working to proactively prevent quality issues.
- With VortiSieze, you can:
- Define and implement aggressive data policies, continuously assess data quality and repair data problems.
- Improve data by parsing, standardizing, and cleansing data from any source, domain, or type.
- Enhance data with internal or external sources to maximize the value of your data.
- Consolidate data to uncover hidden relationships and provide a single version of the truth.
Nearly every organization today uses analytics. But not every organization is getting as much out of its analytics as it could. So, how do you truly excel with analytics to deliver the best support for decisions?
- Don’t fail to plan: Doesn’t sound like a secret at all does it? Well, too feworganizations have spent the time to begin with the end in mind. The most successful companies always begin their analytics projects with a clear vision of what is the target. The key stakeholders should be aligned by writing down and sharing:
- What you’re trying to achieve
- Who you’re trying to reach
- Why it matters
- How you’ll measure success
- Use your analytics tool to uncover data quality issues: Don’t let the desire for perfect data be the barrier to very good data. Instead, use your analytics tools to spot abnormalities in your data and learn from them. Then, work with the people who own the data and share your insights; thus helping them fix their processes. By forming partnerships, you can significantly improve your data quality over time.
- Use Good Design: Most of your data consumers visualize data to understand it, so aesthetics play an important role. Like an interior decorator, a good designer can help you develop an intuitive and effective user experience and a great look and feel for dashboards and visualizations. However, data visualization best practices always outrank aesthetic design – every time.
- Repetition, repetition & repetition – learn through play & through doing: Your worst data model is your first one – nobody creates a perfect model for their data on the first try. And that’s OK. Truth is, looking at your data from different angles can teach you a lot about it. Let everyone connect with the data in their own ways — you’ll be amazed at what they discover. Use what they do to inform your strategy (back to #1).
- Be your loudest evangelist! Some software projects are mandatory for users, however, adoption of analytics is voluntary in most organizations. So, if you want people to know you have built a better mouse trap, act like Guy Kawasaki and start promoting it. Recruit your marketing department and sell the value of analytics throughout your entire team and organization.
- You need a champion: Find an influential person or team that has an unmet need and empower them with analytics. This can turn them into true believers by showing them what’s possible. Then turn the spotlight on their success to prove the value of analytics to the rest of your business.
- Build a Cross Functional Team: Selling analytics is simple when it becomes easy to repeat successes and avoid failures. Bring together a cross-functional team and put them in charge of:
- Deciding the role of analytics
- Defining the standards and tools
- Identifying best practices and gaps
- Iterating and improving the solution over time
- Have dual processes: Changing the method of measuring KPIs or profits requires taking your time and getting it right. However, sometimes you have a unique and urgent situation and must develop an app right now to analyze it. Put in place different processes for both scenarios — and accept the fact that it’s OK to build temporary throw-away apps for one‑off projects.
- Reports are so 90’s: Don’t be like most BI deployments that tend to focus on delivering the same out-of-date reports that has been around for decades. Simply describing the situation presented in the data does not provide analytic value for decision makers. You must answer the ‘why?’, not just the ‘what?’. So, shift your efforts to emphasize diagnostic discovery and exploration capabilities.
- What is your data worth? Are you sitting on the proverbial goldmine with your information? Would outside organizations (internal or external to your company) pay good money to gain access to your proprietary data? Or, as some large retailers do, can you use it to add value for your customers or vendors? Take a step back and see the forest – think creatively about all the ways you could monetize the data you already own.